This disclosure relates generally to patient monitoring. More particularly, the present invention relates to management of alarms triggered during patient monitoring.
Patient monitors are electronic devices designed to display physiological information about a subject. Electrocardiogram (ECG), electroencephalogram (EEG), plethysmographic signals, and signals related to blood pressure, temperature, and respiration represent typical physiological information contained in full-size patient monitors. Patient monitors are typically also furnished with alarming functionality to alert the nursing staff when a vital sign or physiological parameter of a patient exceeds or drops below a preset limit. Alarms are normally both audible and visual effects aiming to alert the staff to a life-threatening condition or to another event considered vital. In most monitors, the alarm limits may be defined by the user, since the limits typically depend on patient etiology, age, gender, medication, and various other subjective factors. Each specific physiological parameter, such as heart rate or blood pressure, may also be assigned more than one alarm limit.
In addition to individual sensor/parameter alarms, patient monitors can be configured to raise combinatory alarms. That is, several physiological parameters may be used to determine a combined index and to give an alarm when the combined index fulfills a specific criterion. The combinatory alarms may range from simple combinations like “low heart rate and low arterial pressure” to complex rule-based scenarios used in various clinical expert systems. These systems help the medical staff to use standardized guidelines and treatment procedures and support the medical staff in clinical decision-making. However, due to the complexity of the built-in intelligence of such systems, it may be difficult for a clinician to grasp the connection between an alarm and the underlying physiological behavior of the patient.
Since it is difficult for a caregiver to control a plurality of stand-alone devices and to interpret the information obtained from a plurality of devices, present patient monitoring devices are often integrated devices in which many capabilities are integrated and in which the built-in intelligence helps the caregiver to get an overall picture of the true status of the patient. For example, monitoring devices used in operating theatres are often provided with ventilation and drug delivery facilities, so that a single monitoring device may offer integration through the entire treatment period.
Due to the integration, these devices are provided with an increasing amount of user-adjustable control parameters, such as ventilation and drug therapy control parameters, to adapt the care processes to the current status of the patient concerned. The care processes are during the course of treatment continuously optimized to give the patient as safe and high quality therapy as possible.
However, the devices are not fully automated closed loop control devices, but user action is needed in response to an alarm event. The problem related to the alarm events is the adaptation of the care processes to the current situation. When an alarm is triggered, the user(s) do not normally know straight away, what would be the optimal way of recovering from the alarm situation to get the patient and the monitoring process back to normal alarm-free state. Therefore, users tend to control the care processes through trial and error. The particular user setting that needs to be controlled to achieve a certain output is not always clearly identifiable. That is, the new values that a user sets for the control parameters in response to an alarm tend to cause overshoot and/or undershoot and therefore also new alarms. The relations between input control parameters and output variables are sometimes simple linear first order relations. However, a change in the operation point may cause non-proportional effects. For example, a change in the end expiratory pressure of lung ventilation may cause non-proportional effects to the respiration due to recruitment of lung alveoli. More complex non-linear physiological systems with difficulties with time constant estimation involve gas exchange and drug concentration changes, which may lead to neurological and hemodynamic changes. Due to the above reasons, the users are not always able to adapt the care processes smoothly to the current situation and the present-day open-loop patient monitors are not able to assist the users in this task to ensure smooth transfer from an alarm event to normal state.